Continuous Bag-of-Words (CBoW)

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• Bag-of-Words (BOW)
• Natural Language Processing (NLP)
• Word2Vec
• Skip-Gram

• Scikit-learn Machine Learning in Python, Simple and efficient tools for data mining and data analysis; Built on NumPy, SciPy, and matplotlib
• Term Frequency, Inverse Document Frequency (TF-IDF)
• Doc2Vec

• Global Vectors for Word Representation (GloVe)
• Feature Exploration/Learning

The CBOW model architecture tries to predict the current target word (the center word) based on the source context words (surrounding words). Considering a simple sentence, “the quick brown fox jumps over the lazy dog”, this can be pairs of (context_window, target_word) where if we consider a context window of size 2, we have examples like ([quick, fox], brown), ([the, brown], quick), ([the, dog], lazy) and so on. Thus the model tries to predict the target_word based on the context_window words. A hands-on intuitive approach to Deep Learning Methods for Text Data — Word2Vec, GloVe and FastText | Dipanjan Sarkar - Towards Data Science